Really appreciate all the love and feedback on V1 and V2. I've been working on the next release, and this time it's a full checkpoint: a merge of my custom Realism Checkpoint and V3 LoRA.

The focus is still on natural skin textures and a wider range of skin tones, without that overly smooth, plastic look. It also gives you a bit more creative freedom now… let's just say being specific about clothing in your prompts is a good idea

A few tips before you try it:

• Natural language works best. Describe the scene, lighting, clothing, and expression you want. Short prompts work too, but leave more room for interpretation.
• Generate at 2MP if you can. It was trained on high-resolution images and looks best there. At 1MP, you might see some minor noise; RTX upscaling nodes or similar post-processing can help.
• Euler + Simple, 4–8 steps. Six steps is my sweet spot.

You can drop it into your existing Krea 2 pipeline as a checkpoint replacement.

I'm also putting together a full ComfyUI workflow and a custom node for some prompting techniques I've been developing. I'll share those soon. For now, there are example images on the model pages so you can see what it can do.

Give it a try and share your results! Feedback on the earlier versions helped a lot, so I'd love to hear what works for you and what still needs improvement. Cheers!

Links:

• Previous Reddit post — V2
• Download on Hugging Face
• Civitai — checkpoint and example images

Uncompressed image: https://postimg.cc/gallery/6WBdT03 (To view the uncompressed image, click on it a second time after opening it, or simply download the entire image album)

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After these tests, I’m confused, and I don’t understand why the Flux 3 Klein model exists or why it turned out the way it did.

Our test subjects

Flux 2 Klein (9B) (Distill) (BF16)

Flux 3 Klein (D) - Distill (BF16)

Flux 3 Klein (B) - Base, NoDistill (BF16)

I took equivalent models for comparison, Flux 3 Klein Distill and Flux 2 Klein (9B), and it’s more correct and fair to compare exactly these two with each other. Still, I decided to add Flux 3 Klein (Base) to understand what this model is capable of overall in its maximum configuration.

For the tests, I used the most standard ComfyUI workflows. I also didn’t use the prompt enhancement feature: my tests include both good, correct prompts and overly abstract, deliberately badly written ones, in order to check how the model handles normal prompts and bad ones, and how well it can generate from short prompts that don’t contain enough information.

Test categories

I split the tests into several categories with different goals:

• Images 1-3: complex prompts with lots of details and different poses. These show how well a model follows the prompt and how often it loses details.
• Images 4-6: typography, mixing typography with real-world imagery, and banner creation.
• Images 7-11: styles. Pay attention to how well each model handles different styles.
• Images 12-13: level of detail. This is similar to tests 1–3, but with a stronger focus on small details.
• Image 14: character understanding.
• Images 15-17: your favorite (or most hated) category, 1girl. Here you can judge overall realism and the ability to generate in an amateur-photo style.

Settings (RTX 3090)

Flux 2 Klein (9B) - 8 steps, 1 CFG (1MP) - generation time (00:07)

Flux 3 Klein (D) - 15 steps, 1 CFG (1MP) - generation time (00:12)

Flux 3 Klein (B) - 50 steps, 3.5 CFG (1MP) - generation time (1:22 - 1:24)

By the way, Flux 2 has a very good VAE, and it’s really sad that Black Forest didn’t update the VAE to version 3 as well :(

My opinion

Flux 3 has the same problems as Flux 2, namely limb mutations. Of course, I didn’t include images with mutations in the tests, but believe me: I ran each generation 2-5 times to get a more or less normal image, and what you see in the tests is the best I got out of those 2-5 generations. For example, for the Nike advertisement image I had to generate about 10 times, at least, to get a normal image without mutations and with physically correct objects in the frame.

Judging by the tests, I can say that the Flux 3 Klein model, specifically the base one without distillation, produces some of the worst images I’ve ever seen, not counting Z-image Base. I assume Flux 3 Klein (Base) exists only for training LoRAs on it, because it’s simply not suitable for T2I tasks. Perhaps it would perform better in an I2I (Edit) scenario, but I’m not sure about that.

Flux 3 Klein Distill gives better results than Flux 3 Base, but it still performs worse than Flux 2 Klein (9B). That’s exactly why I wrote at the beginning that I consider the release of Flux 3 pointless, at least in the form it’s in now, especially when we have such decent models as Krea2, Qwen 2.1 and even BooguImage in the T2I space.

With Flux 3 Klein (Distill), we have anatomy problems, even worse than on F2K, poor detail in eyes and faces, and generally poor detail in some places (Artifacts and blurring in areas with excessive detail). Perhaps the low detail in eyes and other areas could be fixed by raising the megapixel count from 1 to 2-3 during generation, but I think 1MP is the most basic setting and it should always work well, especially since Flux 2 Klein 9B had no problems at all with the “1MP” setting when generating content. Continuing the comparison with Flux 2: on some images Flux 3 has slightly better stylization, but it still has far more drawbacks than advantages to choose it as a replacement for Flux 2 Klein.

I could write a lot more here, but I think the test images will give more information, since everything is pretty obvious anyway. But again, the final choice and the final opinion always remain yours, so I’m looking forward to your comments to hear your thoughts on Flux 3 Klein.

https://civitai.red/models/2997793/kroma-krea-2

It does not get enough attention imo even though its good at everything. The only thing it lacks is knowing artist names compared to Anima.

I don't know if it's just me, or if Krea2 has raised the bar so high that our expectations for diffusion models have completely changed.

I've tested Qwen 2.1 Image and now Flux3 Klein, and honestly, both feel off. The quality, prompt adherence, and overall details just aren't there.

Flux is still recycling those same old textures we've all gotten used to. And no, I'm not even talking about that infamous plastic look, that's old news at this point. I'm talking about the overall image quality, the finer details, and how the model interprets prompts.

It's gotten to the point where Flux2 Klein genuinely feels superior to Flux3 Klein, which is pretty ridiculous considering we're supposed to be moving forward, not backward.

At this point, I'm just hoping Krea's team releases Krea3 soon, because if Krea2 is anything to go by, I genuinely believe Krea3 could absolutely crush both Qwen and Flux.

Just my two cents.

Black Forest Labs (BFL), the frontier AI research lab building visual intelligence, today announced the open weights release of FLUX 3 Image Klein, its most capable open weights image model to date. FLUX 3 Image Klein provides the quality of a large frontier model with the customization and speed of open weights, runs on consumer GPUs and laptops, and retains features from the larger FLUX 3 Image model such as native 4K generation, bounding boxes, and pixel perfect editing.

The release includes a base model for fine-tuning, a distilled model for low-latency generation, and quantized FP8, NVFP4, and INT8 versions. Following September’s open weights release of FLUX 3 Action for robotics, today’s release brings the company’s latest research to core creative use cases, giving developers and businesses greater freedom to build, customize, and deploy visual AI.

Making its research and technology available for others to build on is one of Black Forest Labs’ founding principles. BFL has consistently released its research findings alongside open weight versions of its industry-leading FLUX models, enabling others to build on its advances. So far in 2026, the team has published its Self-Flow research and released open weights for the FLUX.2 [klein] family in January and FLUX 3 Action in September. Self-Flow improved generation across images, video, and audio, while helping models learn representations that can also support robot control. This research led to BFL’s most capable model to date, FLUX 3, which brings multimodal generation and action prediction into a shared architecture. Today’s release opens access to the creative capabilities of the same FLUX 3 backbone, with further open weights releases from the FLUX 3 family to follow.

https://huggingface.co/collections/black-forest-labs/flux-3-image-klein

108M parameter denoiser text to image model. Meant as a research experiement but still produces very good images for it's size.

Samples in the readme alongside how to run.

Is there something wrong with the model implementation in Comfy? Or is it the models themselves? Because my results are terrible worse than F2 Klein. According to their benchmarks, F3K far outperform F2K.

Prompt Example 1: now the man is sitting on a sofa in a modern and minimalist living room.

Prompt Example 2: get the right-side view of the scene.

Hey folks!

Aritra here from Hugging Face. I wanted to mention that Black Forest Labs has recently open sourced their FLUX.3 models on Hugging Face Hub. It has Diffusers Day-0 support!

Here is everything that you would need to know: https://huggingface.co/blog/flux-3

https://huggingface.co/collections/black-forest-labs/flux-3-image-klein

Hey all, another video based on my LongTakeNoCuts nodes and workaround

https://github.com/xyzDist/H3-LongTakeNoCuts

Video: https://www.youtube.com/watch?v=Ktp9U1JI-LA

My YT: https://www.youtube.com/@noiseflowAI

* Yes, she is singing in korean but in Japan, don't ask me why. 😄

details:

• 1 character reference image + ref2va workflow + motion context, some of my nodes.
• hybrid model b20-49
• kitchen attention, no lora, 10 step
• input audio, some silly lyrics then generate a song in SUNO
• 19 * 10s clips, total around 3mins length
• 0.7mp resolution, each clip gen time about 11mins (base 10 step gen + refine 5 step resample)
• I stitch clips in post by CapCut. much simpler and easier than Davinci Resolve
• color grade in post, maybe too much on lowering the contrast.

apply same idea as my workaround refine sample, but this time, add some tweak to it, I used character masking, so it only refine on the character, as this is a moving shot, the background is fine without refining.

If I don't refine the character, it won't completely degrade, but it will drift and get cooked into an ugly face.

Some issues and findings:

• H3 will be lazy if the seed noise can't blend well with previous latent, for me about 3 out of 10 shots it will do the hard cut instead of contiune camera movement, I just need to dice seed and lock it for the shot.
• occasionally some brightness flicker comes from motion-context
• I spend lots of time to figure why H3 ref2va face is so contrast, the dark area is just pure black without details, if you using turbo lora and higher step it will be even worst.....
• There is a 'fizgig H3 tweak' node, it can make the face and video softer
  • https://www.reddit.com/r/StableDiffusion/comments/1wpw247/comfyuifizgigh3tweaks/
  • I only use on the base gen (not refine sample)
    • details and contrast = -0.1
    • mode = per-frame
    • scene variation = 0.1
• the signboards and all the japanese in BG is nonsense and many other error here and there.
• the camera pan out and pan back BG change issue I fixed by extract some image in video for adding as reference image, It worked even better than I expected, and surprisingly straight forward.
• I can do a upscale latent, I will try it later.

So Conclusion, we can generate H3 long duration without degradation, issue sorted out.

You can do whatever interesting cinematic/action long duration oner shot.

Edit: I need to update my github

Heads up: this is far from an academic analysis. It started as a quick check out of my own curiosity and grew a bit from there. The research was mostly guided by AI (Claude), which suggested the comparisons, labelled the images, ran the stats and helped write this up. Treat it as a hobby experiment with interesting patterns, not proof.

TL;DR

• Nudity without asking: with no clothing word in a full-body prompt, 34 of 77 women came out nude or topless. That's stock Krea 2 with no LoRA, and mostly the Eastern European nationalities (17 of 21).
• "Fully clothed" isn't enough: some subjects stay nude anyway. Naming an actual garment worked every time.
• Nationality doesn't change the face much: all 45 European nationalities come out as about 2 faces. Without hair or eye words, nobody is blonde, not even the Swedes.
• Be specific: the detailed prompt held everything in check, and the sparse prompts let the model's defaults leak in.

Nudity

• All nude images come from stock Krea 2 Turbo with no LoRAs. No filter-bypass LoRA was used for them. The krea2filterbypass3 LoRA was used only for the head-and-shoulders portraits in grid 1, which contain no nudity.
• With "full-body photograph of a 30-year-old {woman}, standing barefoot" and no clothing word, 34 of 77 came out nude or topless.
• It's mostly Eastern Europe: 17 of 21 former Eastern Bloc nationalities, against 8 of 24 for the rest of Europe and 9 of 32 outside Europe.
• "Fully clothed" doesn't fix it. The Russian, Albanian and Brazilian parda subjects were still nude, and 6 others got underwear. It's not a seed fluke: on other seeds the Russian was nude on 6 of 7, and the Albanian on 5 of 6.
• Naming an actual garment works: 0 nudes in 231 images.
• Why? Probably training data: a lot of nude and glamour content online features Eastern European models, often captioned with their nationality. Krea doesn't document filtering of its training data.

Prompting takeaways

1. Name the garment, not a state. "Fully clothed" still gave 3 nude and 6 underwear images. A named garment ("light summer clothes", "bra and briefs") gave 0 nudes in 231. At cfg 1 there's no negative prompt to fall back on.
2. A nationality isn't a look. "Swedish woman" alone came out with brown hair. If you want blonde or blue-eyed, write it out explicitly, along with skin tone.
3. Same seed, same face. Swapping nationalities on one seed doesn't give you a cast of different people. Likely fix (untested): vary the seed and describe the face itself.
4. Always state the skin tone. Without skin words, most subjects drifted darker, but South Asians came out lighter.
5. Watch for words with double meanings. "Maltese" added a Maltese dog and white hair. Likely fix (untested): "woman from Malta".
6. Spell out mixed heritage. "Japanese-Nigerian" rendered as fully one parent each time, never mixed. Likely fix (untested): describe the mixed features you want.
7. Specific prompts suppress the priors. The detailed portrait prompt (grid 1) got outfit, backdrop and expression right on 77 of 77. Every deviation came from the sparse prompts.
8. Know the house defaults. Whatever you leave out, Krea fills in the same way:
   • Outfit: "dressed" gives a gray striped tee with black trousers.
   • Background: none specified gives a plain studio wall or white seamless.
   • Hair: in portraits with no hair instruction, it's pulled back on all 77 women (grid 4). "Hair loose and natural" fixed that (grid 1).
9. Scene logic beats "barefoot". It's obeyed in the studio, but every outdoor summer image has sandals. If footwear matters, set the scene to match.

Europe = one face

• Full descriptor (grid 1): the 45 European nationalities are about 2 faces, a "northern" one and a "southern/Balkan" one. Hair and eye colour from the prompt are most of what tells them apart. A face-recognition model confirms the split.
• Name only (grid 4): every European has brown hair pulled back, and there's not a single blonde, Swedes and Finns included. Nationality survives only as light eyes on about 11 Nordic and Baltic women.
• Non-European names keep a distinct, recognisable face across prompts, except in the Middle East: Lebanese and Egyptian are the most similar pair of all 77, and with the name only, the 5 MENA women are even more alike than the Europeans.
• In short: "blonde hair, blue eyes" in the prompt does almost all the work, and "Swedish" barely does anything.

"Dressed" = Western casual

• "Dressed" gives 43 of 45 Europeans the same gray striped tee.
• Traditional dress and headwear appear only outside Europe: all 3 South Asians, hijabs, headwraps, a Quechua hat.

Caveats

• One seed per subject.
• Groupings like "Eastern Bloc" were spotted first and tested afterwards.
• Clothing, age, build and setting were labelled by an AI model (Claude) with no human double-check. NudeNet and a face-recognition model are the independent checks.
• Krea's training data is undocumented, so any "why" is a guess.

Setup

• Generation (ComfyUI):
  • Diffusion model: Krea 2 Turbo (krea2_turbo_fp8_scaled)
  • Text encoder: Qwen3-VL 4B (qwen3vl_4b_fp8_scaled)
  • VAE: Wan 2.1 (Wan2_1_VAE_fp32)
  • LoRA: none for grids 2–7, including every nude image. krea2filterbypass3 at 1.0 was used in grid 1 only (portraits).
  • Sampling: 8 steps, cfg 1, euler/beta, 1024×1536
• Analysis:
  • Nudity check: NudeNet 3.4
  • Faces: YuNet (detection) + SFace (face recognition), from OpenCV Zoo
  • Labelling clothing, age, build and setting: Claude
• Seed: one fixed seed for every image. Only the woman's nationality or ethnicity changes.
• Subjects: 77 women, 45 European and 32 from everywhere else.
• Prompts:
  • Grids 1–3: full descriptor ("Polish woman, fair skin, dark blonde hair, gray-green eyes").
  • Grids 4–7: name only ("Polish woman").
• Gallery: one grid per prompt, split into Europe and the rest. Each grid's header shows the exact prompt.

Flux 3 klien make me remember SD3 body horror. but for other things look nice. will need further testing

I'm downloading the weights right now; I decided to check out the examples on the BFL website. It looks like they didn't check the generated images at all. Why does she have three legs???

I think this model will be very strong. But why are there three legs in the example image? This is a problem of the SD1.5 and SDXL era.

I want to try flux 3 klein as soon as possible, but I have a slow internet.

Just a quick update to say Flux 3 is in and fully supported across every feature.

Cheers,

Pete

I know people aren't posting their videos made with MiniMax H3 anymore... Which is really sad, but I wanted to share mine still! I made a lot of upgrades to my usual workflow and added some gaussian blur to the live action scenes along with film grain (all using KDEnlive) which removes some of the oily skin of AI video in general! This was made with a vertical orientation, sorry about that!

I wanted to see if it was possible to recreate this Genjutsu spam (from Higgsfield) you get all over Tiktok (it is my understanding that it's a combo between Claude and Seedance 2.5) but with Minimax inside Comfy. They usually get a viral tiktok dance and replace the person with an animal or another character.

I used the default ref2va template workflow but added this lora: https://huggingface.co/akatz-ai/MiniMax-H3-Character-Swap-LoRA. In my experience, it transfers the performance more accurately than any other methods I've tried (and I come from Liveportrait, urgggh). Not at the level of Seedance, but hey, this one is local.

I worked shot by shot. Multiple shots worked really bad. These shots were generated at 352x640, with 8 steps using the Minimax_h3_ref2v_turbo_8step lora. The average time was 250 secs per shot on a 4090. First I generated a single frame to guide the result, using the default Qwen 2.1 edit template workflow.

It's VERY important that the loaded video is 24 fps (you can force this frame rate in the loader), otherwise it will be out of sync and doing all kind of weird shenanigans.

As a side note, I vibecoded a node that gets a video from Tiktok or youtube and makes it ready as the reference, with a little timeline included so that it does not load the whole thing.

A simple, random comparison... just for fun.

I can provide the prompt for the first image: A Western man sitting on the beach, building a house of cards atop a purple flag that bears the inscription "Krea 3" in red.

Krea 3 understands subtle nuances of facial expression, such as biting the lower lip, better than other models, but it can also generate unsettling images from simple prompts, all the more so because these images appear realistic.

Hey guys,

so Klein dropped and of course I had to throw it on my machine right away :D I ran both bf16 versions against each other with 50 prompts, same seeds, around 2MP, on an RTX A4500 with 20GB. Base with 50 steps and CFG 3.5, distilled with 15 steps and CFG 1, both with the official ComfyUI workflows. Left is base, right is distilled. These are my 10 favorites.

I didn’t use the prompt upsampler but wrote all prompts in the same caption style it uses, so medium first, then framing, people, background, light and text in quotes. Makes a big difference, short prompts give you pretty generic stuff.

Speed wise base took around 148 seconds per image and distilled around 24. So 50 images were about 2 hours vs 22 minutes, and on most prompts you really have a hard time telling which one is which.

Text is really good. The chinese sign, the japanese one, VISIT MARS, the chalkboard with the euro price, all correct in both. Only a neon HOTEL sign in the rain got messed up by both. Base wins when prompts get tricky though. I asked for robbers with cheap rubber masks of an old bald man and base gives you masks while distilled just gives you four creepy real old dudes :D Counting is still meh, I asked for seven elephants and got 6 and 5. I didnt cherry pick, so there are a lot of errors involved.

And yes I tested the not safe part too, I know you’re gonna ask :D Artistic not safe art :P works somewhat in base, not perfect but good enough that I think you can work with it, especially for finetunes and loras. Distilled is way more restrictive, it either skips nipples completely or just gives you clothes you never asked for to cover things up :D

For me distilled is the everyday model because of the speed and base is the one to build on

Just threw some of my usual testing prompts into it to get a feel for the model - I have to say, it's not entirely without merit in terms of atmosphere, most models struggle quite a bit more with concepts like snowstorms.

But the details tend to be pretty messed up compared to other current models and when it comes to anything living ... yeah, I don't think the model has ever seen proportions.

Drama3 support in audio.cpp was contributed by https://github.com/JarodMica. Many thanks for the great work!

So far, follow-up optimizations have primarily focused on CUDA. The model also runs on Vulkan and Metal. There's still plenty of room for improvement in performance and VRAM efficiency and correctness. On Vulkan, one request hit the token limit. I also found some unexpected silence in multi-speaker generation.

There will be more iterations before the model makes it into a minor release.

If you're interested in helping us further optimize the model, contributions are very welcome! Please ensure that any changes touching the shared framework are additive and opt-in, so we don't have to spend time testing for potential regressions across the other 100+ models that could be affected by changes to shared code.

RTX 5090, Q4_k: